Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1026–1050 of 2167 papers

Title	Date	Tasks	Status	Hype
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering	Dec 16, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified	0
CLIPPO: Image-and-Language Understanding from Pixels Only	Dec 15, 2022	Contrastive Learningimage-classification	—Unverified	0
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory	Dec 10, 2022	Image CaptioningLanguage Modeling	CodeCode Available	0
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners	Dec 9, 2022	Question AnsweringRetrieval	—Unverified	0
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations	Dec 8, 2022	Explanation GenerationVisual Entailment	CodeCode Available	1
ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian	Dec 7, 2022	Image CaptioningQuestion Answering	—Unverified	0
Hierarchical multimodal transformers for Multi-Page DocVQA	Dec 7, 2022	DecoderQuestion Answering	CodeCode Available	1
Review of Ansatz Designing Techniques for Variational Quantum Algorithms	Dec 7, 2022	Visual Question Answering (VQA)	—Unverified	0
InternVideo: General Video Foundation Models via Generative and Discriminative Learning	Dec 6, 2022	Action ClassificationAction Recognition	CodeCode Available	4
Unifying Vision, Text, and Layout for Universal Document Processing	Dec 5, 2022	Document AIdocument understanding	CodeCode Available	3
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests	Dec 3, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning	Dec 2, 2022	DecoderLanguage Modeling	—Unverified	0
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning	Dec 1, 2022	Domain GeneralizationQuestion Answering	CodeCode Available	1
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks	Nov 30, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Optimizing Explanations by Network Canonization and Hyperparameter Search	Nov 30, 2022	Explainable Artificial Intelligence (XAI)image-classification	—Unverified	0
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals	Nov 29, 2022	Deep LearningQuestion Answering	—Unverified	0
Neuro-Symbolic Spatio-Temporal Reasoning	Nov 28, 2022	AI AgentImage Segmentation	—Unverified	0
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning	Nov 24, 2022	cross-modal alignmentImage-text Retrieval	CodeCode Available	1
Self-supervised vision-language pretraining for Medical visual question answering	Nov 24, 2022	Contrastive LearningImage-text matching	CodeCode Available	1
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images	Nov 23, 2022	Optical Character Recognition (OCR)Question Answering	—Unverified	0
A Short Survey of Systematic Generalization	Nov 22, 2022	SurveySystematic Generalization	—Unverified	0
X^2-VLM: All-In-One Pre-trained Model For Vision-Language Tasks	Nov 22, 2022	AllCross-Modal Retrieval	CodeCode Available	2
Cross-Modal Contrastive Learning for Robust Reasoning in VQA	Nov 21, 2022	Contrastive LearningQuestion Answering	CodeCode Available	0
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations	Nov 21, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference	Nov 21, 2022	Natural Language InferenceQuestion Answering	—Unverified	0

Show:10 25 50

← PrevPage 42 of 87Next →

All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified